A method of detection of a recurrent feature of interest within a signal including: obtaining evidence, based on a signal, the evidence including a probability density function for each of a plurality of parameters for parameterizing the signal, including at least one probability density function for a parameter, of the plurality of parameters, that positions a feature of interest within signal data of the signal; parameterizing a portion of the signal data from the signal based upon a hypothesis that a point of interest in the signal data is a position of the feature of interest; determining a posterior probability of the hypothesis being true given the portion of the signal data by combining a prior probability of the hypothesis and a conditional probability of observing the portion of the signal data given the hypothesis.
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3. A method as claimed in claim 1, wherein updating at least one of the probability density functions for the plurality of parameters using the parameterization of the portion of the signal data comprises: updating at least the probability density function for the parameter that positions the feature of interest within the signal data before determining the posterior probability of the hypothesis being true.
10. The apparatus as claimed in claim 9, wherein the at least one memory further stores instructions that, when executed by the at least one processor, cause the apparatus at least to perform: using one or more machine learning algorithms to identify anomalies in the electrocardiogram signal and warn the subject via an audible warning that an anomaly has been detected or that a threshold has been exceeded.
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December 10, 2018
November 12, 2024
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